Skill UI

This skill teaches the critical principles and decision-making processes for safe production releases. It covers platform selection (e.g., Kubernetes, Vercel), mandatory pre-deployment verification (Code Quality, Environment), structured workflows (Prepare, Backup, Deploy, Verify), and advanced strategies like Blue-Green and Canary deployments. Focuses on thinking through risk mitigation, not just executing scripts.

A comprehensive guide to configuring and managing Groq API access across development, staging, and production environments. This skill establishes environment-specific configurations, ensuring optimal model selection, rate limit handling, and secure secret management (using AWS, GCP, Vault) for robust and scalable LLM integrations.

A comprehensive guide to maximizing Groq API performance. This skill details advanced techniques including optimal model selection (balancing latency and quality), implementing semantic caching, efficient token management, streaming responses for perceived speed, and orchestrating parallel requests to ensure high throughput and low latency in production applications.

This skill serves as a comprehensive reference guide for Kling AI's various generative models. It details capabilities across video generation (T2V, I2V), image styling, lip sync, and virtual try-on. Users can compare model versions based on quality, speed, and cost to select the optimal model for their creative or production pipeline.

This comprehensive guide helps developers make critical architectural decisions in modern Node.js development. It covers framework selection (Fastify, Hono, Express), implementing layered architecture, mastering async patterns, robust error handling, and essential security protocols. The focus is on teaching architectural thinking and problem-solving skills, ensuring the development of scalable, high-performance, and maintainable backend APIs.

This skill provides advanced strategies for managing large language model (LLM) context windows and token usage via OpenRouter. Functions include context window lookup, context-aware model selection, conversation trimming, document chunking for RAG, and prompt caching. Use this when dealing with long conversations or large documents to maximize quality while minimizing API costs and preventing context limit overflow.

Provides Terraform and OpenTofu best-practice guidance for module design, testing selection, CI/CD pipeline setup, security scanning, and architecture decisions in production infrastructure-as-code projects.

This skill facilitates the end-to-end deployment of web applications and websites, supporting full-stack architectures including backend APIs, databases, and file storage. It is used when the user wishes to publish a project to a public, live URL, managing the entire deployment lifecycle from template selection to status checking and updates.

This skill serves as a comprehensive senior social media strategist, guiding users through building, auditing, and optimizing their entire online presence. It covers strategic planning, platform selection, defining content pillars, generating structured content calendars, and implementing best practices for community engagement to achieve measurable business outcomes.

This skill covers implementing complex user interactions within Three.js scenes. It demonstrates techniques like raycasting for precise object selection and click detection, handling mouse and touch inputs, and integrating various camera controls (OrbitControls, FlyControls, FirstPersonControls). Use this when building highly interactive, immersive 3D web applications.

This skill guides the comprehensive evaluation and selection of Threat Intelligence Platforms (TIPs). It assesses vendor capabilities, including STIX/TAXII support, workflow automation, SIEM/SOAR integration, and total cost of ownership. It is essential for procurement decisions, migrating between TIP solutions, or assessing whether the current CTI program meets required maturity levels.

This skill is designed for running comprehensive evaluations of Hugging Face Hub models on local hardware. It supports multiple evaluation frameworks, including `inspect-ai` and `lighteval`, and provides flexibility in backend selection (e.g., `vllm`, Transformers, or `accelerate`) to optimize for performance and compatibility. Use this for local GPU benchmarking, smoke testing, and backend selection, but note that it does not handle remote job orchestration or result publishing.